Case Studies

  • Camilo CaraveoEmail author
  • Fevrier Valdez
  • Oscar Castillo
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)


In this chapter we present the different cases of study that are used and analyzed to test the performance and efficiency of the meta-heuristics developed in this book. Five case studies were tested using the optimization algorithm bio-inspired on the self-defense mechanisms of the plants in nature, and the case studies are presented below:


  1. 1.
    Yang, X. S. (2013). Multiobjective firefly algorithm for continuous optimization. Engineering with Computers, 29(2), 175–184.CrossRefGoogle Scholar
  2. 2.
    Yang, X. S. (2010). Firefly algorithm, stochastic test functions and design optimisation. International Journal of Bio-Inspired Computation, 2(2), 78–84.CrossRefGoogle Scholar
  3. 3.
    Liang, J. J., Qu, B. Y., Suganthan, P. N., & Chen, Q. (2014). Problem definitions and evaluation criteria for the CEC 2015 competition on learning-based real-parameter single objective optimization. Technical Report 201411A, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore.Google Scholar
  4. 4.
    Molina, D., & Herrera, F. (2015, May). Iterative hybridization of DE with local search for the CEC’2015 special session on large scale global optimization. In 2015 IEEE Congress on Evolutionary Computation (CEC) (pp. 1974–1978). IEEE.Google Scholar
  5. 5.
    Caraveo, C., Valdez, F., & Castillo, O. (2015). A new bio-inspired optimization algorithm based on the self-defense mechanisms of plants. In Design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization (pp. 211–218). Springer International Publishing.Google Scholar
  6. 6.
    Caraveo, C., Valdez, F., & Castillo, O. (2015). Bio-inspired optimization algorithm based on the self-defense mechanism in plants. In Advances in artificial intelligence and soft computing (pp. 227–237). Springer International Publishing.Google Scholar
  7. 7.
    Johanyák, Z. C., & Papp, O. (2012). A hybrid algorithm for parameter tuning in fuzzy model identification. Acta Polytechnica Hungarica, 9(6), 153–165.Google Scholar
  8. 8.
    Melin, P., Castillo, O., Gonzalez, C. I., Castro, J. R., & Mendoza, O. (2016, October). General Type-2 fuzzy edge detectors applied to face recognition systems. In Fuzzy Information Processing Society (NAFIPS), 2016 Annual Conference of the North American (pp. 1–6). IEEE.Google Scholar
  9. 9.
    Ochoa, P., Castillo, O., & Soria, J. (2016, September). Fuzzy differential evolution method with dynamic parameter adaptation using type-2 fuzzy logic. In 2016 IEEE 8th International Conference on Intelligent Systems (IS) (pp. 113–118). IEEE.Google Scholar
  10. 10.
    Precup, R. E., David, R. C., Petriu, E. M., Preitl, S., & Rădac, M. B. (2014). Novel adaptive charged system search algorithm for optimal tuning of fuzzy controllers. Expert Systems with Applications, 41(4), 1168–1175.CrossRefGoogle Scholar
  11. 11.
    Singla, J. (2015, March). Comparative study of Mamdani-type and Sugeno-type fuzzy inference systems for diagnosis of diabetes. In 2015 International Conference on Advances in Computer Engineering and Applications (ICACEA) (pp. 517–522). IEEE.Google Scholar
  12. 12.
    Zi, B., Sun, H., & Zhang, D. (2017). Design, analysis and control of a winding hybrid-driven cable parallel manipulator. Robotics and Computer-Integrated Manufacturing, 48, 196–208.CrossRefGoogle Scholar
  13. 13.
    Caraveo, C., Valdez, F., Castillo, O., & Melin, P. (2016, December). A new metaheuristic based on the self-defense techniques of the plants in nature. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1–5). IEEE.Google Scholar
  14. 14.
    González, C. I., Castro, J. R., Martínez, G. E., Melin, P., & Castillo, O. (2013, June). A new approach based on generalized type-2 fuzzy logic for edge detection. In IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint (pp. 424–429). IEEE.Google Scholar
  15. 15.
    González, C. I., Melin, P., Castro, J. R., Castillo, O., & Mendoza, O. (2016). Optimization of interval type-2 fuzzy systems for image edge detection. Applied Soft Computing, 47, 631–643.CrossRefGoogle Scholar
  16. 16.
    Olivas, F., Valdez, F., Castillo, O., Gonzalez, C. I., Martinez, G., & Melin, P. (2017). Ant colony optimization with dynamic parameter adaptation based on interval type-2 fuzzy logic systems. Applied Soft Computing, 53, 74–87.CrossRefGoogle Scholar
  17. 17.
    Barbosa, A. M. (2015). fuzzySim: Applying fuzzy logic to binary similarity indices in ecology. Methods in Ecology and Evolution, 6(7), 853–858.CrossRefGoogle Scholar
  18. 18.
    Gupta, A. K., & Sardana, N. (2015, August). Significance of clustering coefficient over Jaccard index. In 2015 Eighth International Conference on Contemporary Computing (IC3) (pp. 463–466). IEEE.Google Scholar
  19. 19.
    Hi, R., Ngan, K. N., & Li, S. (2014, October). Jaccard index compensation for object segmentation evaluation. In 2014 IEEE International Conference on Image Processing (ICIP) (pp. 4457–4461). IEEE.Google Scholar
  20. 20.
    Ramli, N., & Mohamad, D. (2010, December). Fuzzy evaluation based on Jaccard with degree of optimism ranking index. In 2010 International Conference on Science and Social Research (CSSR) (pp. 970–974). IEEE.Google Scholar
  21. 21.
    Melin, P., Olivas, F., Castillo, O., Valdez, F., Soria, J., & Valdez, M. (2013). Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic. Expert Systems with Applications, 40(8), 3196–3206.CrossRefGoogle Scholar
  22. 22.
    Amador-Angulo, L., Mendoza, O., Castro, J. R., Rodríguez-Díaz, A., Melin, P., & Castillo, O. (2016). Fuzzy sets in dynamic adaptation of parameters of a bee colony optimization for controlling the trajectory of an autonomous mobile robot. Sensors, 16(9), 1458.CrossRefGoogle Scholar
  23. 23.
    Caraveo, C., Valdez, F., & Castillo, O. (2016). Optimization of fuzzy controller design using a new bee colony algorithm with fuzzy dynamic parameter adaptation. Applied Soft Computing, 43, 131–142.CrossRefGoogle Scholar
  24. 24.
    Kennedy, J. (2011). Particle swarm optimization. In Encyclopedia of machine learning (pp. 760–766). USA: Springer.Google Scholar
  25. 25.
    Kıran, M. S., & Fındık, O. (2015). A directed artificial bee colony algorithm. Applied Soft Computing, 26, 454–462.CrossRefGoogle Scholar
  26. 26.
    Paré, P. W., & Tumlinson, J. H. (1999). Plant volatiles as a defense against insect herbivores. Plant Physiology, 121(2), 325–332.CrossRefGoogle Scholar
  27. 27.
    Zi, B., Zhu, Z. C., & Du, J. L. (2011). Analysis and control of the cable-supporting system including actuator dynamics. Control Engineering Practice, 19(5), 491–501.CrossRefGoogle Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Camilo Caraveo
    • 1
    Email author
  • Fevrier Valdez
    • 1
  • Oscar Castillo
    • 1
  1. 1.Division of Graduate StudiesTijuana Institute of TechnologyTijuanaMexico

Personalised recommendations